Repo context selection
Rank which files are actually relevant to a task before spending context (and tokens) on them.
"Repository retrieval and context selection" is a core coding-agent pattern: an agent shouldn't stuff every candidate file into the model's context — it's slow, expensive, and dilutes attention. Instead, score each retrieved file for how relevant it is to the task and keep only the ones that clear a bar. A Jev score is ideal here because it's a compact, calibrated judgment you can threshold and audit, run over many files in parallel for a fraction of a cent each — so the agent reads the three files that matter, not the thirty the retriever returned.
Try it live
This is the real thing, not a mockup. Edit the input, hit Run, and Jev returns every typed answer in one round trip — free, no signup. Now picture the same call fired across thousands of items in parallel.
Pick a demo, tweak the input, and hit Run Jev.
The decisions Jev makes
In a single call, Jev evaluates each of these — in parallel, against the same input:
How relevant is this file to completing the task?
rates it on an ordered scale:
- irrelevant
- loosely related
- relevant
- central to the fix
Should this file be included in the agent's working context for this task?
returns a calibrated yes/no probability.
Is this file a likely place the fix will need to be made?
returns a calibrated yes/no probability.
The exact request
This is the real payload behind the live demo — copy it, change the state, and you're building:
{
"model": "jev-latest",
"state": "Task: \"Fix the bug where refunds over the order total are silently accepted.\"\n\nCandidate file surfaced by retrieval: `services/billing/refund.py`\n\nSnippet:\n\n def process_refund(order, amount):\n # TODO: validate against remaining balance\n gateway.refund(order.id, amount)\n record_refund(order, amount)",
"questions": {
"relevance": {
"type": "score",
"instructions": "How relevant is this file to completing the task?",
"criteria": [
"irrelevant",
"loosely related",
"relevant",
"central to the fix"
]
},
"include": {
"type": "noul",
"instructions": "Should this file be included in the agent's working context for this task?"
},
"likely_edit_site": {
"type": "noul",
"instructions": "Is this file a likely place the fix will need to be made?"
}
}
}Wire it into your code
Read the typed answers and branch in plain code — no parsing. Auto-handle the high-confidence cases and route the uncertain ones to a bigger model or a human. It's one API call and output is free, so ask every question you need at once.
Build your own
Every scenario above is a single API call. Try any of them free in the playground, then get a hosted key to ship it in minutes.